Enhancing Few-Shot Image Classification via Contrastive Representation Learning and Meta-Knowledge Transfer

Few-shot image classification presents a significant challenge due to the scarcity of labeled data. This paper proposes a novel approach that leverages contrastive representation learning to extract robust feature embeddings from limited examples and incorporates meta-knowledge transfer to enhance generalization capabilities. We adapt contrastive learning objectives to optimize feature space similarity for classes with few examples, and we introduce a meta-learning framework to transfer knowledge from auxiliary datasets to improve the classification performance on novel, unseen categories. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method, achieving state-of-the-art performance in few-shot scenarios.